Female-dominated disciplines have lower evaluated research quality and funding success rates, for men and women

  1. Alex James  Is a corresponding author
  2. Franca Buelow
  3. Liam Gibson
  4. Ann Brower
  1. School of Mathematics and Statistics, University of Canterbury, New Zealand
  2. Bioprotection Centre of Research Excellence, University of Lincoln, Lincoln, New Zealand
  3. School of Earth and Environment, University of Canterbury, New Zealand
3 figures, 3 tables and 8 additional files

Figures

Male-dominated disciplines have higher expected research scores than female-dominated disciplines in all three Performance-Based Research Fund (PBRF) assessments.

Points – the mean raw score of individuals in each of the disciplines (full raw data cannot be shown for privacy reasons), against the proportion of men in the discipline (blue – men, red – women). …

Research score is strongly correlated with number of research outputs (A), which is correlated to the gender balance of the discipline (B); but, after adjusting for a typical number of research outputs, female-dominated disciplines still have lower scores (C).

Performance-Based Research Fund (PBRF) data using individuals at University of Canterbury (UC) (N=384). (A) Score against number of research outputs over the period of the PBRF assessment. Trend …

Researchers in male-dominated disciplines have a higher chance of funding success.

(A) Australian Research Council (ARC) (2010–2019): Points – funding success rates by gender in 20 disciplines over 10 years against proportion of men in the discipline in 2018. Lines – expected …

Tables

Table 1
A summary of the four independent datasets used in the study showing: country, time period of the data, number of disciplines, research evaluation method, number of individuals or applicants by gender, number of individual data points, output variable, and mean expected output for each gender across the whole dataset.
SchemePBRFARCCIHREIGE
CountryAotearoa New ZealandAustraliaCanadaPredominantly EU
Time span2000–062007–122013–182010–192011–162019
Disciplines4242432248
EvaluationResearch quality score (200–700)Funding success
Women17082658329746,231814349,863
Men252240054181130,33115,77585,305
Data points42306663748744016333
OutcomeMean scoreSuccess rate
Women27236837925.7%14.7%27.0%
Men35142443327.1%16.4%30.3%
Table 2
Maximal candidate model and best fit model to predict Score for Performance-Based Research Fund (PBRF) and logit(PSuccess) for each dataset.

Wilkinson notation is used to indicate interaction terms.

ResponseMaximal modelBest model (lowest AIC)
PBRFScoreAge+(1|Inst)+f(Gender,pmen)Age+(1|Inst)+Genderpmen
ARClogit(P(grantsuccess))f(Gender,pmen,Time)(Gender+pmen)Time
CIHRf(Gender,pmen,Time)Gender+pmen+Time
EIGE(1|Country)+f(Gender,pmen)(1|Country)+Genderpmen
Appendix 1—table 1
A summary of the three Performance-Based Research Fund (PBRF) datasets.

Mean (and standard deviation) of score and age at each time point. ***p<0.001, *0.01<p<0.05, two-sided t-test men/women.

Time period20062007–122013–18
Disciplines424243
NWomen170826583297
Men252240054181
Mean score (std)Women272 (161)***368 (139)***379 (142)***
Men351 (163)424 (143)433 (146)
Mean age (std)Women44.5 (9.6)*47.8 (10.4)***47.7 (11.1)***
Men45.3 (10.1)50.0 (10.9)49.5 (11.4)

Additional files

Supplementary file 1

Table S1: Comparative descriptions of each of the four datasets.

https://cdn.elifesciences.org/articles/97613/elife-97613-supp1-v1.docx
Supplementary file 2

Table S2: Summary statistics of the Performance-Based Research Fund (PBRF) data by discipline.

Includes the number of women and men, mean age, and mean score for each gender and proportion of men in each discipline.

https://cdn.elifesciences.org/articles/97613/elife-97613-supp2-v1.xlsx
Supplementary file 3

Detailed model output for all Performance-Based Research Fund (PBRF), Australian Research Council (ARC), Canadian Institute of Health Research (CIHR), and European Institute of Gender Equality (EIGE) data candidate models.

Each sheet shows the coefficients and p-values for the full range of candidate models trialled for each dataset to predict either Score (PBRF) or Funding success (ARC, CIHR, EIGE). Coefficient names (first column) are given in Wilkinson’s notation. ΔAIC is the change in AIC between the best fit model (minimum AIC, ΔAIC = 0) and the candidate model. r-Squared is also given for the linear model (PRF only). Individual country coefficients and p-values for the EIGE data are not shown for clarity.

https://cdn.elifesciences.org/articles/97613/elife-97613-supp3-v1.xlsx
Supplementary file 4

Detailed model output for the bibliometric models.

Sheet 1. Single bibliometric models ScoreAge+Gender+log(Bib) .The coefficients and p-values for each of the five bibliometrics. ΔAIC is the change in AIC between the best fit model, Noutputs with minimum AIC and ΔAIC = 0, and the candidate model. r-Squared is also given. Sheet 2. Two bibliometric model ScoreAge+Gender+log(Noutputs)+log(Bib) .The coefficients and p-values for each of the other four bibliometrics. ΔAIC is the change in AIC between the Noutputs only model (minimum AIC, ΔAIC = 0) and the candidate model. r-Squared is also given. Sheet 3. Coefficients and p-values for ScoreAge+Genderpmen+log(Noutputs).

https://cdn.elifesciences.org/articles/97613/elife-97613-supp4-v1.xlsx
MDAR checklist
https://cdn.elifesciences.org/articles/97613/elife-97613-mdarchecklist1-v1.docx
Source data 1

DataARC.xlsx.

Field, Gender, Year, Number of applicants, Number of successes, proportion men (from external data).

https://cdn.elifesciences.org/articles/97613/elife-97613-data1-v1.xlsx
Source data 2

DataCIHR.xlsx.

Field, Gender, Type of grant, Time period (before or after funding change), Number of applicants, number of successes, proportion male (from application numbers).

https://cdn.elifesciences.org/articles/97613/elife-97613-data2-v1.xlsx
Source data 3

DataEIGE.xlsx.

Country, Field, Gender, Number of applicants, Number of successes, proportion male (from applicant data), proportion male from external data.

https://cdn.elifesciences.org/articles/97613/elife-97613-data3-v1.xlsx

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